2007-k-db-dudley-compressing data oracle - … partitioned table with bitmap index in 3 partitions...

1Carl Dudley – University of Wolverhampton, UK

Data Compression in Oracle

Carl Dudley

University of Wolverhampton, UK

UKOUG SIG Director

[email protected]


Main Topics

� Oracle 9i and 10g Compression - major features

� Compression in data warehousing

� Sampling the data to predict compression

� Pre-sorting the data for compression

� Behaviour of DML/DDL on compressed tables

� Compression Internals

� Advanced Compression in Oracle11g (for OLTP operations)

� Shrinking unused space


Oracle Data Compression – Main Features


Compression : Characteristics

� Trades Physical I/O against CPU utilization

– Transparent to applications

– Can increase I/O throughput and buffer cache capacity

� Useful for 'read mostly' applications

– Decision Support and OLAP

� Compression is performed only when Oracle considers it worthwhile

– Depends on column length and amount of duplication

� Compression algorithms have caused little change to the Kernel code

– Modifications only to block formatting and accessing rows and columns

– No compression within individual column values or across blocks

• Blocks retrieved in compressed format in the buffer cache


Getting Compressed

� Building a new compressed table

CREATE TABLE <table_name> ...

COMPRESS;

CREATE TABLE <table_name> COMPRESS AS SELECT ...

� Altering an existing table to be compressed

ALTER TABLE <table_name> MOVE COMPRESS;

– No additional copy created but temp space and exclusive table level lock required for the compression activity

ALTER TABLE <table_name> COMPRESS;

– Future bulk inserts may be compressed – existing data is not

� Compressing individual partitions

ALTER TABLE <table_name>

MOVE PARTITION <partition_name> COMPRESS;

– Existing data and future bulk inserts compressed in a specific partition


Tablespace Level Compression

� Entire tablespaces can compress by default

– All objects in the tablespace will be compressed by default

CREATE | ALTER TABLESPACE < tablespace_name>

DEFAULT [ COMPRESS | NOCOMPRESS ] ...


Compressing Table Data

� Uncompressed conventional emp table

� Could consider sorting the data on columns which lend

themselves to compression

CREATE TABLE emp

(empno NUMBER(4)

,ename VARCHAR2(12)

...)

COMPRESS;

� Compressed emp table

CREATE TABLE emp

(empno NUMBER(4)

,ename VARCHAR2(12)

...);


7369 CLERK 2000 1550 ACCOUNTING

7782 MANAGER 4975 1600 PLANT

7902 ANALYST 4000 2100 OPERATIONS

7900 CLERK 2750 1500 OPERATIONS

7934 CLERK 2200 1200 ACCOUNTING

7654 PLANT 3000 1100 RESEARCH

Table Data Compression

� Uncompressed emp table

� Compressed emp table

– Similar to index compression

[SYMBOL TABLE] [A]=CLERK, [B]=ACCOUNTING, [C]=PLANT,

[D]=OPERATIONS

7369 [A] 2000 1550 [B]

7782 MANAGER 4975 1600 [C]

7902 ANALYST 4000 2100 [D]

7900 [A] 2750 1500 [D]

7934 [A] 2200 1200 [B]

7654 [C] 3000 1100 RESEARCH


Observing Compression Information

� Compressed tables are not identified in user_tables in Oracle9i

– Fixed in Oracle10g

� Create a new view called my_user_tables by modifying the create view statement for user_tables in the catalog.sql file (see italic type)

� Create a public synonym for the new view and grant public access to it

create or replace view MY_USER_TABLES

(TABLE_NAME, TABLESPACE_NAME, CLUSTER_NAME, IOT_NAME,

PCT_FREE, PCT_USED,

:

GLOBAL_STATS, USER_STATS, DURATION, SKIP_CORRUPT, MONITORING,

CLUSTER_OWNER, DEPENDENCIES,COMPRESSED)

as

select o.name, decode(bitand(t.property, 4194400), 0, ts.name, null),

decode(bitand(t.property, 1024), 0, null, co.name),

:

decode(bitand(t.flags, 8388608), 8388608, 'ENABLED', 'DISABLED'),

decode(bitand(s.spare1, 2048), 2048, 'ENABLED', 'DISABLED')

from sys.ts$ ts, sys.seg$ s, sys.obj$ co, sys.tab$ t, sys.obj$ o,

sys.obj$ cx, sys.user$ cu

:


SELECT COUNT(ename) uncompressed FROM ec;

UNCOMPRESSED ------------

229376 Elapsed: 00:00:04.00

SELECT COUNT(ename) compressed FROM ec1;

COMPRESSED ----------

229376 Elapsed: 00:00:02.07

Table Data Compression (continued)

� Scanning the compressed table is much faster

– Compressing can significantly reduce disk I/O - good for queries

• Possible increase in CPU activity

• Must unravel the compression but logical reads will be reduced

SELECT table_name,compressed,num_rows FROM my_user_tables;

TABLE_NAME COMPRESSED NUM_ROWS---------- ---------- --------EC DISABLED 229376

EC1 ENABLED 229376


Space Reduction Due to Compression

� Space usage summary

Statistics for table EC1Free Blocks.............................0

Total Blocks............................384

Total Bytes.............................3145728

Unused Blocks...........................46

Unused Bytes............................376832

Last Used Ext FileId....................1

Last Used Ext BlockId...................54025

Last Used Block.........................82

Statistics for table ECFree Blocks.............................1

Total Blocks............................1438

Total Bytes.............................11534336

Unused Blocks...........................58

Unused Bytes............................475136

Last Used Ext FileId....................1

Last Used Ext BlockId...................53641

Last Used Block.........................70

– Summary routine adapted from Tom Kyte’s example


Compression in Data Warehousing


Compression in Data Warehousing

� Fact tables are good candidates for compression

– Large and have repetitive values

– Repetitive data tends to be clustered

• More clustered than in TPC-H tables

� Dimension tables are often too small for compression

� Large block size leads to greater compression

– Typical in data warehouses

– More rows available for compression within each block

� Materialized views can be compressed (and partitioned)

– Naturally sorted on creation due to GROUP BY

– Especially good for ROLLUP views and join views

• Tend to contain repetitive data


Compression of Individual Table Partitions

� Partition level

– Partitioning must be range or list (or composite)

– The first partition will be compressed

– Could consider compressing read only partitions of historical data

CREATE TABLE sales

(sales_id NUMBER(8)

: :

,sales_date DATE)

PARTITION BY RANGE(sales_date)

(PARTITION sales_jan2003 VALUES LESS THAN

(TO_DATE('02/01/2003','DD/MM/YYYY')) COMPRESS,

PARTITION sales_feb2003 VALUES LESS THAN

(TO_DATE('03/01/2003','DD/MM/YYYY')),

PARTITION sales_mar2003 VALUES LESS THAN

(TO_DATE('04/01/2003','DD/MM/YYYY')),

PARTITION sales_apr2003 VALUES LESS THAN

(TO_DATE('05/01/2003','DD/MM/YYYY')));


Effect of Partition Operations

� Consider individual partitions compressed as shown

– Produces two new compressed partitions

PARTITION p1a COMPRESS VALUES LESS THAN 50

PARTITION p1b COMPRESS VALUES LESS THAN 100

PARTITION p2 COMPRESS VALUES LESS THAN 200

PARTITION p3 NOCOMPRESS VALUES LESS THAN 300


ALTER TABLE s1 SPLIT PARTITION p1 (50)

INTO (PARTITION p1a, PARTITION p1b);

� Splitting a compressed partition






Effect of Partition Operations (contd)

� Effect of merging compressed partitions

– New partition p1b_2 is not compressed by default

• Same applies if any to be merged are initially uncompressed


PARTITION p1b COMPRESS VALUES LESS THAN 100





PARTITION p1b_2 NOCOMPRESS VALUES LESS THAN 200



ALTER TABLE s1 MERGE PARTITIONS p1b,p2

INTO PARTITION p1b_2;

� Merge of two compressed partitions


Forcing Compression During Partition Maintenance

ALTER TABLE s1 MERGE PARTITIONS p2,p3

INTO PARTITION p2_3 COMPRESS;

� Force compression of new partition after a merge operation

ALTER TABLE s1 SPLIT PARTITION p1 AT (50)

INTO (PARTITION p1a COMPRESS,PARTITION p2a);

� Force compression of the new partition(s) after a split operation

� Partitions may be empty or contain data during maintenance

operations involving compression


Effect of Partitioned Bitmap Indexes

� Scenario :

– Table having no compressed partitions has bitmap locally partitioned indexes

– The presence of usable bitmap indexes will prevent the first operation that compresses a partition

SQL> ALTER TABLE sales MOVE PARTITION p4 COMPRESS;

ORA-14646: Specified alter table operation involving compression

cannot be performed in the presence of usable bitmap indexes

SQL> ALTER TABLE part2 SPLIT PARTITION p1a AT (25)

INTO ( PARTITION p1c COMPRESS,PARTITION p1d);

ORA-14646: Specified alter table operation involving compression

cannot be performed in the presence of usable bitmap indexes


� Uncompressed partitioned table with bitmap index in 3 partitions

Compression of Partitions with Bitmap Indexes in Place

CREATE TABLE emp_part

PARTITION BY RANGE (deptno)

(PARTITION p1 VALUES LESS THAN (11),

PARTITION p2 VALUES LESS THAN (21),

PARTITION p3 VALUES LESS THAN (31))

AS SELECT * FROM emp;

CREATE BITMAP INDEX part$empno

ON emp_part(empno) LOCAL;


� First compression operation requires the following

1. Mark bitmap indexes unusable (or drop them)

Compression of Partitions with Bitmap Indexes in Place (continued)

3. Rebuild the bitmap indexes (or recreate them)

ALTER INDEX part$empno UNUSABLE;

2. Compress the first (and any subsequent) partition as required

ALTER TABLE emp_part MOVE PARTITION p1 COMPRESS;

ALTER INDEX part$empno REBUILD PARTITION p1;



– Each index partition must be individually rebuilt


� Oracle needs to know maximum records per block

– Correct mapping of bits to blocks can then be done

– On compression this value increases

� Oracle has to rebuild bitmaps to accommodate potentially larger number of values even if no data is present in the partition(s)

– Could result in larger bitmaps for uncompressed partitions

• Increase in size can be offset by the actual compression

� Once rebuilt, the indexes can cope with any compression

– Subsequent compression operations do not invalidate bitmap indexes

� Recommended to create each partitioned table with at least one compressed (dummy/empty?) partition

– Can be subsequently dropped

� Compression activity does not affect Btree usability

Compression of Partitions with Bitmap Indexes in Place (continued)


Table Level Compression for Partitioned Tables

� Compression can be the default for all partitions

CREATE TABLE sales

(sales_id NUMBER(8),

: :

sales_date DATE)

COMPRESS

PARTITION BY (sales_date)

...

– Can still specify individual partitions to be NOCOMPRESS

� Default partition maintenance actions on compressed tables

– Splitting non-compressed partitions results in non-compressed partitions

– Merging non-compressed partitions results in a compressed partition

– Adding a partition will result in a new compressed partition

– Moving a partition does not alter its compression


Finding the Largest Tables

SELECT owner

,name

,SUM(gb)

,SUM(pct)

FROM (SELECT owner

,name

,TO_CHAR(gb,'999.99') gb

,TO_CHAR((RATIO_TO_REPORT(gb) OVER())*100,'999,999,999.99') pct

FROM (SELECT owner

,SUBSTR(segment_name,1,30) name

,SUM(bytes/(1024*1024*1024)) gb

FROM dba_segments

WHERE segment_type IN ('TABLE','TABLE PARTITION')

GROUP BY owner

,segment_name

)

)

WHERE pct > 3

GROUP BY ROLLUP(owner

,name)

ORDER BY 3;

� Useful for finding candidates for compression


Finding the Largest Tables (contd)

OWNER NAME SUM(GB) SUM(PCT)

------------- -------------- ------------- ----------

SH COSTS .03 8.23

SH SALES .05 14.44

SH SALES_HIST .13 32.93

SH .21 55.61

SYS IDL_UB2$ .01 3.86

SYS SOURCE$ .02 6.43

SYS .03 10.29

.24 65.90


Sampling Data to Predict Compression


Compression Factor and Space Saving

� Compression Factor (CF)

compressed blocks

non-compressed blocks* 100

compressed blocks

non-compressed blocks - compressed blocks* 100

CF =

SS =

� Space Savings (SS)


Predicting the Compression Factor

CREATE OR REPLACE FUNCTION compression_ratio (tabname VARCHAR2)

RETURN NUMBER IS

pct NUMBER := 0.000099; -- sample percentage

blkcnt NUMBER := 0; -- original block count (should be < 10K)

blkcntc NUMBER; -- compressed block count

BEGIN

EXECUTE IMMEDIATE ' CREATE TABLE temp_uncompressed PCTFREE 0 AS SELECT *

FROM ' || tabname || ' WHERE ROWNUM < 1';

WHILE ((pct < 100) AND (blkcnt < 1000)) LOOP -- until > 1000 blocks in sample

EXECUTE IMMEDIATE 'TRUNCATE TABLE temp_uncompressed';

EXECUTE IMMEDIATE 'INSERT INTO temp_uncompressed SELECT * FROM ' ||

tabname || ' SAMPLE BLOCK (' || pct || ',10)';

EXECUTE IMMEDIATE 'SELECT COUNT(DISTINCT(dbms_rowid.rowid_block_number(rowid)))

FROM temp_uncompressed' INTO blkcnt;

pct := pct * 10;

END LOOP;

EXECUTE IMMEDIATE 'CREATE TABLE temp_compressed COMPRESS AS SELECT * FROM

temp_uncompressed';

EXECUTE IMMEDIATE 'SELECT COUNT(DISTINCT(dbms_rowid.rowid_block_number(rowid)))

FROM temp_compressed' INTO blkcntc;

EXECUTE IMMEDIATE 'DROP TABLE temp_compressed';

EXECUTE IMMEDIATE 'DROP TABLE temp_uncompressed';

RETURN (blkcnt/blkcntc);

END;

/


Predicting the Compression Factor (continued)

CREATE OR REPLACE PROCEDURE compress_test(p_comp VARCHAR2)

IS

comp_ratio NUMBER;

BEGIN

comp_ratio := compression_ratio(p_comp);

dbms_output.put_line('Compression factor for table ' ||

p_comp ||' is '|| comp_ratio );

END;

EXEC compress_test('EMP')

Compression factor for table EMP is 1.6

� Run the compression test for the emp table


Compression Test – Clustered Data

CREATE TABLE clust (col1

VARCHAR2(1000))

COMPRESS;

INSERT INTO clust VALUES ('VV...VV');





INSERT INTO clust VALUES ('WW...WW');

: : :

INSERT INTO clust VALUES ('WW...WW');

INSERT INTO clust VALUES ('XX...XX');

: : :

INSERT INTO clust VALUES ('YY...YY');

: : :

INSERT INTO clust VALUES ('ZZ...ZZ');

CREATE TABLE noclust (col1

VARCHAR2(1000))

COMPRESS;

INSERT INTO noclust VALUES ('VV...VV');

INSERT INTO noclust VALUES ('WW...WW');

INSERT INTO noclust VALUES ('XX...XX');

INSERT INTO noclust VALUES ('YY...YY');

INSERT INTO noclust VALUES ('ZZ...ZZ');


INSERT INTO noclust VALUES ('WW...WW');

INSERT INTO noclust VALUES ('XX...XX');

INSERT INTO noclust VALUES ('YY...YY');



: : :


� Every value for column col1 is 390 bytes long

� Both tables have a total of 25 rows stored in blocks of size 2K

– So a maximum of four rows will fit in each block

� Both have same amount of repeated values but the clustering is different


Compression Test (continued)

noclust - 4 rows per block. (7 blocks in total)

The 5th row to be inserted must go in the next block as it contains different data

vv…vv

ww…ww

xx…xx

yy…yy

zz…zz

xx…xx

yy…yy

vv…vv

ww…ww

zz…zz

vv…vv

ww…ww

xx…xx

yy…yy

zz…zz

vv…vv

ww…ww

xx…xx

yy…yy zz…zz

vv…vv

clust - 20 rows per block.

Rows 2,3,4,5 are duplicates of the first row

in the block.

Rows 7,8,9,10 are duplicates of the 6th row

in the block, and this pattern is repeated.

The residual space in the first block is used

by the compressed data

header header header header

header header


Compression Test - Compression Factors

� Compression test routine is accurate due to sampling of actual data

– Make sure default tablespace is correctly set

• Temporary sample tables are physically built for the testing

EXEC compress_test('NOCLUST')

Compression factor for table NOCLUST is 1

EXEC compress_test('CLUST')

Compression factor for table CLUST is 3.5


Testing Compression : Using Repeatable sampling on Oracle9i

� Estimate the compression/decompression ratio for a table, abc

DROP TABLE abc$test1;

DROP TABLE abc$test2;

2. Drop any previously created test tables

ALTER SESSION

SET EVENTS '10193 trace name context forever, level 1';

1. Make sampling repeatable:

– This statement appears to have no effect in Oracle10g


Testing Compression : Further Example (continued)

LOCK TABLE abc IN SHARE MODE;

INSERT /*+ APPEND */ INTO abc$test1

SELECT * FROM abc SAMPLE BLOCK(x,y);

INSERT /*+ APPEND */ INTO abc$test2

SELECT * FROM abc SAMPLE BLOCK(x,y);

5. Place the same test data in each of the test tables

CREATE TABLE abc$test2 NOCOMPRESS AS

SELECT * FROM abc WHERE ROWNUM < 1;

4. Create an empty uncompressed table:

CREATE TABLE abc$test1 COMPRESS AS

SELECT * FROM abc WHERE ROWNUM < 1;

3. Create an empty compressed table:

– This example uses block level sampling


Testing Compression : Sampling Rows

SELECT * FROM emp SAMPLE (10) SEED (1);

• Selects a 10% sample of rows

• If repeated, a different sample will be taken

SELECT * FROM emp SAMPLE (10);

� Tables can be sampled at row or block level

– Block level samples a random selection of whole blocks

– Row level (default) samples a random selection of rows

� Samples can be 'fixed' in Oracle10g using SEED

– SEED can can have integer values from 0 -100

– Can also have higher numbers ending in '00'

– Shows a 10% sample of rows

– If repeated, the exact same sample will be taken

• Also applies to block level sampling

• The sample set will change if DML is performed on the table


Pre-Sorting the Data for Compression


Sorting the Data for Compression

� Presort the data on a column which has : no. of distinct values ~ no. of blocks

� Information on column cardinality is shown in:

ALL_TAB_COL_STATISTICS

ALL_PART_COL_STATISTICS

ALL_SUBPART_COL_STATISTICS

� Reorganize (pre-sort) rows in segments that will be compressed

to cause repetitive data within blocks

� For multi-column tables, order the rows by the low cardinality column(s)

CREATE TABLE emp_comp COMPRESS AS

SELECT * FROM emp ORDER BY <some unselective column(s)>;

– For a single-column table, order the table rows by the column value


Sorting the Data for Compression

� Presort data on column having no. of distinct values ~ no. of blocks~

SELECT COUNT(DISTINCT ename) FROM large_emp; 170 enames

SELECT COUNT(DISTINCT job) FROM large_emp; 5 jobs

SELECT * FROM large_emp; (114368 rows)

EMPNO ENAME JOB------- ------------ ----------43275 25***** CLERK

47422 128**** ANALYST

79366 6****** MANAGER

: : :


Sorting the Data for Compression (continued)

� Sorting on the job column is not the most effective

Compressed table sorted on ename : Number of used blocks = 172

CREATE TABLE cename COMPRESS

AS SELECT empno,ename,job FROM large_emp ORDER BY ename;

Compressed table sorted on job : Number of used blocks = 243

CREATE TABLE cjob COMPRESS

AS SELECT empno,ename,job FROM large_emp ORDER BY job;

Non-compressed table : Number of used blocks = 360

CREATE TABLE nocomp

AS SELECT empno,ename,job FROM large_emp;


Behaviour of DML/DDL on Tables with Default Compression


Default Compressed Table Behaviour

� Updates, and conventional single and multi-row inserts are NOT

compressed

– UPDATE

• Wholesale updates lead to large increases in storage (>250%)

• Performance impact on UPDATEs can be around 400%

• Rows are migrated to new blocks (default value of PCTFREE is 0)

– DELETE

• Performance impact of around 15% for compressed rows

� Creating a compressed table can take 50% longer

� Compressed tables cannot be modified in 9.2.0.1

ORA-22856 cannot add column to object tables


Default Compressed Table Behaviour

(continued)� Operations which 'perform' compression

– CREATE TABLE ... AS SELECT ...

– ALTER TABLE ... MOVE ...

– INSERT /*+APPEND*/ (single threaded)

– INSERT /*+PARALLEL(sales,4)*/

• Requires ALTER SESSION ENABLE PARALLEL DML;

• Both of the above inserts work with data from database tables and external tables

– SQL*Loader DIRECT = TRUE

– Various partition maintenance operations

� Can not be used for:– LOB and VARRAY constructs

– Index organized tables

• Can use index compression on IOTs

– External tables, index clusters, hash clusters

– Bulk loads within PL/SQL

– Logical standby techniques


Compression Internals


Hexadecimal Dump of Compressed Data

Symbol Table :

14 unique names

5 unique jobs

Start of next block


Oracle Dump - Two Uncompressed Employee Rows

...block_row_dump:tab 0, row 0, @0x4a1tl: 41 fb: --H-FL-- lb: 0x2 cc: 8col 0: [ 3] c2 03 38col 1: [ 5] 43 4c 41 52 4bcol 2: [ 7] 4d 41 4e 41 47 45 52col 3: [ 3] c2 4f 28col 4: [ 7] 77 b5 06 09 01 01 01col 5: [ 3] c2 19 33col 6: *NULL*col 7: [ 2] c1 0btab 0, row 1, @0x4catl: 40 fb: --H-FL-- lb: 0x2 cc: 8col 0: [ 3] c2 03 39col 1: [ 5] 53 43 4f 54 54col 2: [ 7] 41 4e 41 4c 59 53 54col 3: [ 3] c2 4c 43col 4: [ 7] 77 bb 04 13 01 01 01col 5: [ 2] c2 1fcol 6: *NULL*col 7: [ 2] c1 15...

ALTER SYSTEM DUMP

DATAFILE 8 BLOCK 35;

� Creates a trace file in USER_DUMP_DEST


tl: 18 fb: --H-FL-- lb: 0x0 cc: 2col 0: [ 9] 50 52 45 53 49 44 45 4e 54col 1: [ 4] 4b 49 4e 47bindmp: 00 0b 02 d1 50 52 45 53 49 44 45 4e 54 cc 4b 49 4e 47tab 0, row 1, @0x746tl: 9 fb: --H-FL-- lb: 0x0 cc: 2col 0: [ 7] 41 4e 41 4c 59 53 54col 1: [ 4] 46 4f 52 44bindmp: 00 0a 02 11 cc 46 4f 52 44.....

tab 0, row 13, @0x6cctl: 10 fb: --H-FL-- lb: 0x0 cc: 2col 0: [ 5] 43 4c 45 52 4bcol 1: [ 5] 53 4d 49 54 48bindmp: 00 0b 02 0e cd 53 4d 49 54 48tab 0, row 14, @0x780tl: 8 fb: --H-FL-- lb: 0x0 cc: 1col 0: [ 5] 43 4c 45 52 4bbindmp: 00 04 cd 43 4c 45 52 4b.....

tab 0, row 17, @0x761tl: 10 fb: --H-FL-- lb: 0x0 cc: 1col 0: [ 7] 41 4e 41 4c 59 53 54bindmp: 00 02 cf 41 4e 41 4c 59 53 54tab 1, row 0, @0x6c3tl: 9 fb: --H-FL-- lb: 0x0 cc: 3col 0: [ 5] 43 4c 45 52 4bcol 1: [ 5] 53 4d 49 54 48col 2: [ 3] c2 02 34bindmp: 2c 00 02 02 0d cb c2 02 34

Oracle Dump of Table of empno,ename,job

PRESIDENT KING

FORD

SMITH

CLERK

ANALYST


Oracle Avoids Unnecessary Compression

� Create two tables with repeating small values in one column

CREATE TABLE tnocomp (

col1 VARCHAR2(1)

,col2 VARCHAR2(6))

PCTFREE 0;

COL1 COL2---- ------1A 1ZZZZZ

2A 2ZZZZZ

3A 3ZZZZZ

4A 4ZZZZZ

5A 5ZZZZZ

1A 6ZZZZZ

2A 7ZZZZZ

...

4A 319ZZZ

5A 320ZZZ

� Insert data (320 rows) as follows

CREATE TABLE tcomp (

col1 VARCHAR2(1)

,col2 VARCHAR2(6))

COMPRESS;

Values unique in col2

Values repeat in col1 every 5 rows


Evidence of Minimal Compression

SELECT dbms_rowid.rowid_block_number(ROWID) block

,dbms_rowid.rowid_relative_fno(ROWID) file

,COUNT(*) num_rows

FROM &table_name

GROUP BY dbms_rowid.rowid_block_number(ROWID)

,dbms_rowid.rowid_relative_fno(ROWID);

BLOCK FILE NUM_ROWS----- ---- --------

66 8 128

67 8 132

68 8 60


34 8 126

35 8 126

36 8 68

tnocomp tcomp

� Evidence of compression in the 'compressed' table


Further Evidence of Compression

ALTER SYSTEM

DUMP DATAFILE 8

BLOCK 67;

block_row_dump:tab 0, row 0, @0x783tl: 5 fb: --H-FL-- lb: 0x0 cc: 1col 0: [ 2] 31 41bindmp: 00 1b ca 31 41tab 0, row 1, @0x77etl: 5 fb: --H-FL-- lb: 0x0 cc: 1col 0: [ 2] 32 41bindmp: 00 1b ca 32 41tab 0, row 2, @0x779tl: 5 fb: --H-FL-- lb: 0x0 cc: 1col 0: [ 2] 33 41bindmp: 00 1b ca 33 41tab 0, row 3, @0x774tl: 5 fb: --H-FL-- lb: 0x0 cc: 1col 0: [ 2] 34 41bindmp: 00 1a ca 34 41tab 0, row 4, @0x76ftl: 5 fb: --H-FL-- lb: 0x0 cc: 1col 0: [ 2] 35 41bindmp: 00 19 ca 35 41tab 1, row 0, @0x763tl: 12 fb: --H-FL-- lb: 0x0 cc: 2col 0: [ 2] 31 41col 1: [ 6] 31 30 31 30 30 30bindmp: 2c 00 02 02 00 c9 31 30 31 30 30 30tab 1, row 1, @0x757tl: 12 fb: --H-FL-- lb: 0x0 cc: 2col 0: [ 2] 32 41col 1: [ 6] 31 30 32 30 30 30bindmp: 2c 00 02 02 01 c9 31 30 32 30 30 30

HEX(A) = 41

Symbol Table (tab0)(1A,2A.3A.4A.5A)


Compression not Performed on Unsuitable Data

� Both tables recreated with values in col1 now set toTO_CHAR(MOD(ROWNUM,50))

– Much less repetition of values (only every 50 rows) allowing less

compression

COL1 COL2---- ------1 1ZZZZZ

2 2ZZZZZ

3 3ZZZZZ

4 4ZZZZZ

... 5ZZZZZ

49 49ZZZZ

50 50ZZZZ

1 51ZZZZ

2 52ZZZZ

3 53ZZZZ


66 8 128

67 8 128

68 8 64


34 8 128

35 8 128

36 8 64

tnocomp

tcompOracle decides not to compress


Comparison of Heap and IOT Compression

IOT = Index Organized table


Comparison of IOT and Heap Tables

� Tests constructed using a standard set of data in emptest

– Six columns with absence of nulls

EMPNO ENAME JOB HIREDATE SAL DEPTNO------ ---------- --------- --------- ------ ------

1 KING PRESIDENT 17-NOV-81 5000 10

2 FORD ANALYST 03-DEC-81 3000 20

3 SCOTT ANALYST 09-DEC-82 3000 20

4 JONES MANAGER 02-APR-81 2975 20

5 BLAKE MANAGER 01-MAY-81 2850 30

6 CLARK MANAGER 09-JUN-81 2450 10

7 ALLEN SALESMAN 20-FEB-81 1600 30

8 TURNER SALESMAN 08-SEP-81 1500 30

9 MILLER CLERK 23-JAN-82 1300 10

10 WARD SALESMAN 22-FEB-81 1250 30

11 MARTIN SALESMAN 28-SEP-81 1250 30

12 ADAMS CLERK 12-JAN-83 1100 20

13 JAMES CLERK 03-DEC-81 950 30

14 SMITH CLERK 17-DEC-80 800 20

15 KING PRESIDENT 17-NOV-81 5000 10

16 FORD ANALYST 03-DEC-81 3000 20

... ... ... ... ... ...

229376 SMITH CLERK 17-DEC-80 800 20

IOT = Index Organized table


Creation of IOTs

� empi : Conventional IOT based on emptest data

CREATE TABLE empi (empno,ename,job,hiredate,sal,deptno,

CONSTRAINT pk_empi PRIMARY KEY(ename,job,hiredate,sal,deptno,empno))

ORGANIZATION INDEX

PCTFREE 0

AS SELECT * FROM emptest

� empic : First five columns compressed

CREATE TABLE empic (empno,ename,job,hiredate,sal,deptno,

CONSTRAINT pk_empic PRIMARY KEY(ename,job,hiredate,sal,deptno,empno))

ORGANIZATION INDEX

PCTFREE 0

COMPRESS 5

AS SELECT * FROM emptest


Test tables

� Average row length obtained from user_tables (avg_row_len)

– Compressed heap tables show no reduction in average row length

Table Table Compress Blocks Average

Name Type row length

emph Heap No 1092 36

emphc Heap Yes 361 36empi IOT No 1077 35

empic IOT Yes 261 7

� Four tables built having heap/IOT structures and

compressed/noncompressed data

� Unique index built on empno column in emph and emphc tables


Compression Data

� Number of blocks in IOTs obtained from index validation

VALIDATE INDEX &index_name;

SELECT * FROM index_stats;

� Compressed IOTs have compression shown as DISABLED in

user_tables, but ENABLED in user_indexes

SELECT COUNT(COUNT(*))

FROM &table_name

GROUP BY dbms_rowid.rowid_block_number(ROWID)

,dbms_rowid.rowid_relative_fno(ROWID);

� Number of blocks in heap tables obtained using dbms_rowid


Timings to Scan Tables

SELECT COUNT(deptno) FROM <table_name>;

Table Table Compress Elapsed

Name Type Time (s)

emph Heap No 0.70

emphc Heap Yes 0.47

empi IOT No 0.37

empic IOT Yes 0.21


Access Paths for Heap Table

EXPLAIN PLAN FOR SELECT * FROM emph WHERE EMPNO > 193387;

SELECT * FROM TABLE(dbms_xplan.display);

PLAN_TABLE_OUTPUT

----------------------------------------------------------------------------

| Id | Operation | Name | Rows | Bytes | Cost (%CPU)|

----------------------------------------------------------------------------

| 0 | SELECT STATEMENT | | 35989| 984K| 252 (2)|

| 1 | TABLE ACCESS BY INDEX ROWID| EMPH | 35989| 984K| 252 (2)|

|* 2 | INDEX RANGE SCAN | PK_EMPH | 35989| | 78 (3)|

----------------------------------------------------------------------------

EXPLAIN PLAN FOR SELECT * FROM emph WHERE EMPNO > 193386;


PLAN_TABLE_OUTPUT

---------------------------------------------------------------


---------------------------------------------------------------


|* 1 | TABLE ACCESS FULL| EMPH | 35990| 984K| 252 (5)|

---------------------------------------------------------------


Access Paths for Compressed Heap Table

EXPLAIN PLAN FOR SELECT * FROM emphc WHERE EMPNO > 206863;


PLAN_TABLE_OUTPUT

-----------------------------------------------------------------------------| Id | Operation | Name | Rows | Bytes | Cost (%CPU)|-----------------------------------------------------------------------------| 0 | SELECT STATEMENT | | 22513| 615K| 86 (3)|| 1 | TABLE ACCESS BY INDEX ROWID| EMPHC | 22513| 615K| 86 (3)||* 2 | INDEX RANGE SCAN | PK_EMPHC | 22513| | 49 (3)|-----------------------------------------------------------------------------

EXPLAIN PLAN FOR SELECT * FROM emphc WHERE EMPNO > 206862;


PLAN_TABLE_OUTPUT

-------------------------------------------------------------------| Id | Operation | Name | Rows | Bytes | Cost (%CPU)|-------------------------------------------------------------------| 0 | SELECT STATEMENT | | 22514| 642K| 91 (11)||* 1 | TABLE ACCESS FULL| EMPHC | 22514| 642K| 91 (11)|-------------------------------------------------------------------

� Note that full table scan is (rightly) more likely to be performed


Access Path for IOTs

EXPLAIN PLAN FOR SELECT * FROM empi WHERE EMPNO > 200000;

SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY);

PLAN_TABLE_OUTPUT

---------------------------------------------------------------------


---------------------------------------------------------------------


|* 1 | INDEX FAST FULL SCAN| PK_EMPI | 29376| 803K| 238 (1)|

---------------------------------------------------------------------


Updates to Heap and IOT Tables

UPDATE <table_name> SET ename = 'XXXXXXX‘;

Table Table Compress Blocks Blocks Increase PCT Elapsed

Name Type before after in blocks increase Time

update update

Emph Heap No 1092 1280 188 ~ 10% 5 mins

Emphc Heap Yes 361 2291 1930 ~ 600% 15 mins

Empi IOT No 1077 2218 1141 ~ 100% 4 mins

Empic IOT Yes 261 527 266 ~ 100% 12 mins

� Note the ‘explosion’ in size of the compressed heap table

� Lengthens each employee name by at least one character


Advanced Compression in Oracle11g

(for OLTP Operations)


Advanced Compression in Oracle 11g

� Conventional DML maintains the compression

– Inserted and updated rows remain compressed

� The compress activity is kept at a minimum

� Known as the Advanced Compression option

– Extra cost


Compressing for OLTP Operations

� Requires COMPATIBILITY = 11.1.0 (or higher)

CREATE TABLE t1 ... COMPRESS FOR ALL OPERATIONS ;

� Conventionally inserted rows stay uncompressed until PCTFREE is reached

– Mimimises compression operations

– Existing rows are not compressedHeader Header Header HeaderHeader

Conventional inserts are notcompressed

Block becomes full

Rows are nowcompressed

More uncompressedInserts fill the block

Rows arecompressed again

Transaction activity

Free space Free space Free space Free space Free space


OLTP Compression

� Some early results

� Table containing 3million parts records

– Avoid large scale (batch) updates on OLTP compressed tables

• Significant processing overheads

16M12M35M

Compression for all operations

Compression for direct path operations

No compression


OLTP Compression Test

� Table containing 500000 sales records

PROD_ID CUST_ID TIME_ID CHANNEL_ID PROMO_ID AMOUNT_SOLD ACCOUNT_TYPE------- ------- --------- ---------- -------- ----------- -------------

13 5590 10-JAN-04 3 999 1232.16 Minor account19 6277 23-FEB-05 1 123 7690.00 Minor account16 6859 04-NOV-05 3 999 66.16 Minor account: : : : : : :

� Three tables created

CREATE TABLE s_non_c PCTFREE 0 AS SELECT * FROM sales;

CREATE TABLE s_c_all COMPRESS FOR ALL OPERATIONS

AS SELECT * FROM sales;

— Non-compressed table with fully packed blocks

CREATE TABLE s_c COMPRESS AS SELECT * FROM sales;

— Compressed table for DSS operations

— Compressed table for OLTP operations


OLTP Compression Test (continued)

2:20:12 secs1:04 secs39 secsElapsed time for update

588855043200Size after update (blocks)

10248963200Original size (blocks)

Compressed Table for OLTP

Compressed Table for DSS

Non-compressed table

� Test somewhat unfair on OLTP compression

– Update is large and batch orientated

– I/O subsystem was single disk

– But still interesting?

UPDATE sales SET account_type = ‘Major account’WHERE prod_id > 13;

� Update stress test – update 94% of the rows

– No lengthening of any data


Shrinking Unused Space in Oracle10g


Reclaiming Space in Oracle10g

� Unused space below the High Water Mark can be reclaimed online

– Space caused by delete operations can be returned to free space

– Object must be in a locally managed tablespace with automatic segment space management (ASSM)

– Removes the need for Online Redefinition to reclaim space?

� Two-step operation

– Rows are moved to blocks available for insert

• Requires row movement to be enabled

– High water mark (HWM) is repositioned

– Newly freed blocks are returned to free space if the COMPACTkeyword is not used

ALTER TABLE <table_name> SHRINK SPACE;

ALTER TABLE <table_name> ENABLE ROW MOVEMENT;


Repositioning of HWM During shrinks

HWM

Allocated block above the high

water mark

HWM

Free block Free block Free block

� Rows are physically moved to space in blocks on the freelist

– Shrinking causes ROWIDs to change

– Indexes (bitmap and btree) are updated accordingly

� Shrinking does not work with compressed tables


Shrinking Objects

� Why Shrink?

– To reclaim space

– To increase speed of full table scans

– To allow access to table during the necessary reorganisation

– The shrink operation can be terminated/interrupted at any time

• Can be continued at a later time from point of termination

� Objects that can be SHRINKed

– Tables

– Indexes

– Materialized views

– Materialized view logs

– Dependent objects may be shrunk when a table shrinks

ALTER TABLE <table_name> SHRINK SPACE [CASCADE];


Data Compression in Oracle

Carl Dudley

University of Wolverhampton, UK

UKOUG SIG Director

[email protected]

2007-k-db-dudley-compressing data oracle - … partitioned table with bitmap index in 3 partitions...

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